Intravoxel incoherent motion (IVIM) imaging is a non-invasive MR perfusion imaging that could prevent patients from the harm of exogenous reagent. Previous studies proved that the least square and Bayesian approaches are so far the best algorithms in IVIM fitting. However, they still suffer from time-consuming and high noise level. We proposed a deep neural network-based reconstruction method with synthetic training data for IVIM imaging and extended it to hybrid IVIM-DKI (diffusion kurtosis imaging) model fitting. Experimental results show that our method owns prominent performance in both image quality and accuracy of fitting results with a remarkably short reconstruction time.